• DocumentCode
    3490334
  • Title

    Identification and Transformation of Comparative Sentences in Patent Chinese-English Machine Translation

  • Author

    Runxiang Zhang ; Yaohong Jin

  • Author_Institution
    Inst. of Chinese Inf. Process., Beijing Normal Univ., Beijing, China
  • fYear
    2012
  • fDate
    13-15 Nov. 2012
  • Firstpage
    217
  • Lastpage
    220
  • Abstract
    Machine translation for patent texts is a worldwide concern and more than 25% of the sentences in patent texts are comparative sentences. So the research on identification and transformation of comparative sentences in patent texts is very significant and can contribute to the patent Chinese-English machine translation. On the issue of identification of comparative sentences, there are some studies basically using statistical methods, but almost no study applies the rule-based method and no study uses the results of identification for Chinese-English machine translation. In this paper, based on HNC (Hierarchical Network of Concepts) theory, we applied the method based on rules to study the identification and transformation of comparative sentences in patent texts. Experiment shows us that the method can improve the performance of patent machine translation effectually.
  • Keywords
    language translation; natural language processing; patents; statistical analysis; text analysis; HNC theory; comparative sentence identification; comparative sentence transformation; hierarchical network-of-concepts; patent Chinese-English machine translation; patent texts; statistical methods; Educational institutions; Information processing; Labeling; Libraries; Patents; Pragmatics; Semantics; comparative sentence; identification; machine translation; rule; transformation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Asian Language Processing (IALP), 2012 International Conference on
  • Conference_Location
    Hanoi
  • Print_ISBN
    978-1-4673-6113-2
  • Electronic_ISBN
    978-0-7695-4886-9
  • Type

    conf

  • DOI
    10.1109/IALP.2012.41
  • Filename
    6473735